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The Effects of Domain and Collaboration Feedback on Learning in a Collaborative Intelligent Tutoring System

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Artificial Intelligence in Education (AIED 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6738))

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Abstract

We present initial results from a study comparing the effects of domain and collaboration feedback on learning within COLLECT-\(\mathcal{UML}\), a collaborative problem-solving ITS. Using COLLECT-\(\mathcal{UML}\), two students in separate physical locations (a collaborative pair) construct UML class diagrams to solve problems together. In the default version, COLLECT-\(\mathcal{UML}\) provides both domain and collaboration feedback. In this study however, collaborative pairs were randomly assigned to one of four modes (treatment conditions) which varied the feedback presented by the system: no feedback (NF), domain feedback only (DF), collaborative feedback only (CF), and both domain and collaborative feedback (DCF). All conditions improved significantly between pre- and post-test, showing that practicing within COLLECT-\(\mathcal{UML}\) helps learning. At a surface level, collaborative pairs in all modes had similar amounts of collaboration. The DCF mode had significantly higher learning gains than the other modes, indicating the value of receiving both domain and collaborative feedback. Surprisingly, the CF mode had the lowest learning gains (lower than NF), suggesting that, in this case, good collaboration without domain feedback could have simply reinforced erroneous domain knowledge.

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References

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Authors and Affiliations

  1. Intelligent Computer Tutoring Group, University of Canterbury, Private Bag 4800, Christchurch, 8140, New Zealand

    Jay Holland, Moffat Mathews & Antonija Mitrovic

  2. Dept. of Computing, Unitec Institute of Technology, Auckland, New Zealand

    Nilufar Baghaei

Authors
  1. Jay Holland
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  2. Nilufar Baghaei
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  3. Moffat Mathews
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  4. Antonija Mitrovic
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Editor information

Editors and Affiliations

  1. EECS Department/ISIS, Vanderbilt University, TN 37235, Nashville, USA

    Gautam Biswas

  2. Electronic, Electrical and Computer Engineering, University of Birmingham, U.K.

    Susan Bull

  3. School of Information Technologies, University of Sydney, 1 Cleveland Street, 2006, Sydney, Australia

    Judy Kay

  4. College of Engineering, Department of Computer Science and Software Engineering, University of Canterbury, Private Bag 4800, 8140, Christchurch, New Zealand

    Antonija Mitrovic

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© 2011 Springer-Verlag Berlin Heidelberg

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Holland, J., Baghaei, N., Mathews, M., Mitrovic, A. (2011). The Effects of Domain and Collaboration Feedback on Learning in a Collaborative Intelligent Tutoring System. In: Biswas, G., Bull, S., Kay, J., Mitrovic, A. (eds) Artificial Intelligence in Education. AIED 2011. Lecture Notes in Computer Science(), vol 6738. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21869-9_72

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  • DOI: https://doi.org/10.1007/978-3-642-21869-9_72

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21868-2

  • Online ISBN: 978-3-642-21869-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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